RESEARCH SUMMARY Women are at more than 1.5-fold higher risk for adverse events, including serious adverse events such as drug induced liver injury. Between 1997 and 2000, eight out of the ten drugs withdrawn from the market proposed greater health risks to women. While some of this increased risk is due to women being overdosed, a large portion of these differences are also related to biological sex differences; however, the mechanisms behind these differences are poorly understand. In addition, an often-cited reason for not studying women is the presence of within-sex variability due to the menstrual cycle. Throughout the drug development pipeline, sex is rarely considered, and labels are routinely left out of analysis, even at the computational level. Genetic data, in the forms of Genome-Wide Association Studies (GWAS) and gene expression levels, provide unique opportunities for analyzing the effects of sex; they allow for insights into biological function and examination of unlabeled data is possible in this case because sex can be easily imputed. Additionally, network-based analysis of these data has the benefit of increasing the signal-to-noise ratio by relying on prior information about gene-gene interactions and pathways, and also aids in the biological interpretation of results. To improve understanding of sex-differential effects, I propose to leverage genetic data to accomplish following specific aims: 1) use gene expression data to investigate the molecular effects of between-sex differences at the organ-level and within-sex differences due to menstrual cycle hormone variability, 2) develop network-based methods for detecting sex-differential effects in GWAS, and 3) link identified between- and within-sex variability to drug response. My work will improve understanding of interactions between sex and drug response, and provide insight into the mechanisms behind these interactions. With success and further evaluation, this analysis will improve the drug development process by taking sex-related variability into account, decreasing adverse events. My long-term career goal is to become an independent academic researcher developing informatics methods to study between and within sex variability in biology, disease, and drug response. During my fellowship training, I will work toward this goal by deepening my research skills, building collaborations, publishing papers, attending seminars and conferences, taking additional relevant coursework, and teaching and mentoring students. I am exceptionally well-poised to achieve these goals; my training will take place with Dr. Russ Altman, who has an extremely successful track record of mentoring students, and at Stanford University, which has incredible educational resources and a collaborative cutting-edge research environment.